Active inference helps us simulate adaptive behavior and decision-making in
biological and artificial agents. Building on our previous work exploring the
relationship between active inference, well-being, resilience, and
sustainability, we present a computational model of an agent learning
sustainable resource management strategies in both static and dynamic
environments. The agent's behavior emerges from optimizing its own well-being,
represented by prior preferences, subject to beliefs about environmental
dynamics. In a static environment, the agent learns to consistently consume
resources to satisfy its needs. In a dynamic environment where resources
deplete and replenish based on the agent's actions, the agent adapts its
behavior to balance immediate needs with long-term resource availability. This
demonstrates how active inference can give rise to sustainable and resilient
behaviors in the face of changing environmental conditions. We discuss the
implications of our model, its limitations, and suggest future directions for
integrating more complex agent-environment interactions. Our work highlights
active inference's potential for understanding and shaping sustainable
behaviors.

主题：积极推理，自适应行为，决策制定，可持续资源管理和环境动态；总结：积极推理可在面对环境变化时产生可持续、有韧性的行为，并对可持续行为的理解和塑造具有潜力。

运用主动推理建模可持续资源管理

Modeling Sustainable Resource Management using Active Inference

Recent advancements in foundation models (FMs) have unlocked new prospects in
autonomous driving, yet the experimental settings of these studies are
preliminary, over-simplified, and fail to capture the complexity of real-world
driving scenarios in human environments. It remains under-explored whether FM
agents can handle long-horizon navigation tasks with free-from dialogue and
deal with unexpected situations caused by environmental dynamics or task
changes. To explore the capabilities and boundaries of FMs faced with the
challenges above, we introduce DriVLMe, a video-language-model-based agent to
facilitate natural and effective communication between humans and autonomous
vehicles that perceive the environment and navigate. We develop DriVLMe from
both embodied experiences in a simulated environment and social experiences
from real human dialogue. While DriVLMe demonstrates competitive performance in
both open-loop benchmarks and closed-loop human studies, we reveal several
limitations and challenges, including unacceptable inference time, imbalanced
training data, limited visual understanding, challenges with multi-turn
interactions, simplified language generation from robotic experiences, and
difficulties in handling on-the-fly unexpected situations like environmental
dynamics and task changes.

基于视频和语言模型的 DriVLMe 智能代理在自动驾驶中表现出相对竞争力，但仍存在推理时间过长、训练数据不平衡、视觉理解能力有限、多轮交互挑战以及处理环境动态和任务变化等实时场景中的困难。

DriVLMe: 用具身化和社交经验增强基于 LLM 的自动驾驶代理

DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and  Social Experiences

Safe Multi-agent reinforcement learning (safe MARL) has increasingly gained
attention in recent years, emphasizing the need for agents to not only optimize
the global return but also adhere to safety requirements through behavioral
constraints. Some recent work has integrated control theory with multi-agent
reinforcement learning to address the challenge of ensuring safety. However,
there have been only very limited applications of Model Predictive Control
(MPC) methods in this domain, primarily due to the complex and implicit
dynamics characteristic of multi-agent environments. To bridge this gap, we
propose a novel method called Deep Learning-Based Model Predictive Control for
Safe Multi-Agent Reinforcement Learning (DeepSafeMPC). The key insight of
DeepSafeMPC is leveraging a entralized deep learning model to well predict
environmental dynamics. Our method applies MARL principles to search for
optimal solutions. Through the employment of MPC, the actions of agents can be
restricted within safe states concurrently. We demonstrate the effectiveness of
our approach using the Safe Multi-agent MuJoCo environment, showcasing
significant advancements in addressing safety concerns in MARL.

基于深度学习的模型预测控制方法被提出，以解决安全多智能体强化学习中存在的复杂多智能体环境动力学问题，该方法在解决多智能体系统的安全问题方面取得了显著进展。

深入学习的安全多智能体强化学习中的模型预测控制

DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe  Multi-Agent Reinforcement Learning

The rapid progress of foundation models has led to the prosperity of
autonomous agents, which leverage the universal capabilities of foundation
models to conduct reasoning, decision-making, and environmental interaction.
However, the efficacy of agents remains limited when operating in intricate,
realistic environments. In this work, we introduce the principles of
$\mathbf{U}$nified $\mathbf{A}$lignment for $\mathbf{A}$gents
($\mathbf{UA}^2$), which advocate for the simultaneous alignment of agents with
human intentions, environmental dynamics, and self-constraints such as the
limitation of monetary budgets. From the perspective of $\mathbf{UA}^2$, we
review the current agent research and highlight the neglected factors in
existing agent benchmarks and method candidates. We also conduct
proof-of-concept studies by introducing realistic features to WebShop,
including user profiles to demonstrate intentions, personalized reranking for
complex environmental dynamics, and runtime cost statistics to reflect
self-constraints. We then follow the principles of $\mathbf{UA}^2$ to propose
an initial design of our agent, and benchmark its performance with several
candidate baselines in the retrofitted WebShop. The extensive experimental
results further prove the importance of the principles of $\mathbf{UA}^2$. Our
research sheds light on the next steps of autonomous agent research with
improved general problem-solving abilities.

介绍了统一对齐原理来提高自主代理的性能，并在 WebShop 的实验中验证了该原则的重要性。